State Key Laboratory of Clean Energy Utilization, Zhejiang University
Institute of Energy and Power Engineering, Zhejiang University of Technology

有机固废热转化过程会发生一系列复杂的热化学反应,给深入理解其机理、优化转化过程技术参数及实现产物定向调控等带来挑战。 基于数据驱动的机器学习建模方法可推动有机固废的智能化和精细化处置。 综述了基于机器学习的智能建模方法在有机固废全链条处置中的应用,总结了机器学习对有机固废上游产生量与理化特性、中游热转化过程及产物特性、下游产物利用与效益评估等的预测并对比了不同模型的适用场景及优缺点,以期构建有机固废全链条智能化处置方案,实现集无害化、减量化、资源化、高值化及智能化于一体的有机固废高效处置模式,为实际固废的有效管理提供一定的指导意义。

A series of complex thermochemical reactions occur in the thermal conversion process of or⁃ganic solid waste, which brings challenges to the in-depth understanding of its mechanism, the optimi⁃zation of technical parameters for the conversion process and the directional regulation of products. Thedata-driven machine learning modeling method can promote the intelligence and refinement of organicsolid waste disposal. In this review, the application of the intelligent modeling method based onmachine learning in the whole chain disposal of organic solid waste was summarized. The prediction ofmachine learning on the upstream production and physical and chemical characteristics of organic solidwaste, the thermal conversion process and product characteristics in the middle stream, and the utiliza⁃tion and benefit evaluation of downstream products were reviewed. The applicable scenarios, advantagesand disadvantages of different models were compared. The purpose was to construct an intelligent dis⁃posal scheme for the whole chain of organic solid waste, and realize an efficient disposal mode of organ⁃ic solid waste integrating harmlessness, reduction, resource utilization, high value and intelligence,and to provide some guidance for the effective management of solid waste.

浙江省“领雁”研发攻关计划资助(2022C03092);国家自然科学基金区域创新联合基金(U21A20142)

张子杭, 许丹, 胡艳军, 等. 机器学习在有机固废全链条处置中的应用进展[J]. 能源环境保护, 2023, 37(1): 184-193.

ZHANG Zihang, XU Dan, HU Yanjun, et al. Application progress of machine learning in the whole chaindisposal of organic solid waste[J]. Energy Environmental Protection, 2023, 37(1): 184-193.